ICJIA R Workshop

Preface

2018-02-13 Bobae Kang (Bobae.Kang@illinois.gov)

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Source: Pixarbay.com

A brief intro to ...

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Source: r-project.org

What is R?

“R is a language and environment for statistical computing and graphics.” - The R Foundation

Built for data analysis and visualization
One of the the most popular choices of programming language among academic researchers and data scientists

Open source (free!)
Built for statistical analysis
Reproducible and transparent
Extensible through powerful third-party libraries
Enabling researchers to tackle a variety of tasks using a single platform

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Source: Time Magazine

Data manipulation

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Source: Wikimedia.org

# peak at the first rows of the data
head(ispcrime_tbl)

# A tibble: 6 x 12
   year county viol~ murd~  rape robb~ aggA~ prop~ burg~ larc~ MVTft arson
  <int> <fctr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1  2011 Adams    218     0    37    15   166  1555   272  1241    36     6
2  2011 Alexa~   119     0    14     4   101   290    92   183    11     4
3  2011 Bond       6     1     0     0     5   211    58   147     5     1
4  2011 Boone     59     0    24     8    27   733   152   563    14     4
5  2011 Brown      7     0     1     0     6    38    14    22     1     1
6  2011 Bureau    42     0     4     3    35   505    90   405     8     2

# get a quick summary of each column
summary(ispcrime_tbl)

      year            county     violentCrime       murder       
 Min.   :2011   Adams    :  5   Min.   :    0   Min.   :  0.000  
 1st Qu.:2012   Alexander:  5   1st Qu.:   19   1st Qu.:  0.000  
 Median :2013   Bond     :  5   Median :   42   Median :  0.000  
 Mean   :2013   Boone    :  5   Mean   :  501   Mean   :  7.026  
 3rd Qu.:2014   Brown    :  5   3rd Qu.:  133   3rd Qu.:  1.000  
 Max.   :2015   Bureau   :  5   Max.   :33348   Max.   :566.000  
                (Other)  :480   NA's   :7       NA's   :7        
      rape            robbery          aggAssault      propertyCrime   
 Min.   :   0.00   Min.   :    0.0   Min.   :    0.0   Min.   :     0  
 1st Qu.:   1.00   1st Qu.:    0.0   1st Qu.:   15.0   1st Qu.:   133  
 Median :   6.00   Median :    2.0   Median :   33.0   Median :   349  
 Mean   :  41.29   Mean   :  172.3   Mean   :  280.4   Mean   :  2913  
 3rd Qu.:  22.00   3rd Qu.:   13.0   3rd Qu.:  102.0   3rd Qu.:  1190  
 Max.   :1986.00   Max.   :16095.0   Max.   :15129.0   Max.   :178902  
 NA's   :7         NA's   :7         NA's   :7         NA's   :7       
    burglary         larcenyTft           MVTft             arson        
 Min.   :    0.0   Min.   :     0.0   Min.   :    0.0   Min.   :   0.00  
 1st Qu.:   35.5   1st Qu.:    85.5   1st Qu.:    3.0   1st Qu.:   1.00  
 Median :   79.0   Median :   258.0   Median :   10.0   Median :   2.00  
 Mean   :  589.3   Mean   :  2084.9   Mean   :  215.2   Mean   :  23.45  
 3rd Qu.:  268.0   3rd Qu.:   852.0   3rd Qu.:   30.0   3rd Qu.:   8.50  
 Max.   :38485.0   Max.   :116145.0   Max.   :22879.0   Max.   :1418.00  
 NA's   :7         NA's   :7          NA's   :7         NA's   :7

# filter to keep only counties starting with C for 2015
#   while creating and showing a new variable for total crime count
ispcrime_tbl %>%
  filter(substr(county, 1, 1) == "C", year == 2015) %>%
  mutate(totalCrime = violentCrime + propertyCrime) %>%
  select(year, county, totalCrime)

# A tibble: 12 x 3
    year county     totalCrime
   <int> <fctr>          <int>
 1  2015 Calhoun            NA
 2  2015 Carroll           176
 3  2015 Cass              154
 4  2015 Champaign        6486
 5  2015 Christian         292
 6  2015 Clark             103
 7  2015 Clay              191
 8  2015 Clinton           423
 9  2015 Coles             805
10  2015 Cook           153575
11  2015 Crawford          282
12  2015 Cumberland         42

# get annual average count of violent crime by county
ispcrime_tbl %>%
  group_by(county) %>%
  summarise(annualAvgCrime = sum(violentCrime, propertyCrime, na.rm = TRUE) / 5)

# A tibble: 102 x 2
   county    annualAvgCrime
   <fctr>             <dbl>
 1 Adams             1724  
 2 Alexander          385  
 3 Bond               190  
 4 Boone              426  
 5 Brown               39.0
 6 Bureau             480  
 7 Calhoun             13.8
 8 Carroll            196  
 9 Cass               109  
10 Champaign         6567  
# ... with 92 more rows

# merging regions data and count the number of rows by region
ispcrime_tbl %>%
  left_join(regions) %>%
  group_by(region) %>%
  count()

# A tibble: 4 x 2
# Groups: region [4]
  region       n
  <fctr>   <int>
1 Central    230
2 Cook         5
3 Northern    85
4 Southern   190

Data visualization

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Source: Wikimedia.org

# line plot of violent crime trend by region
ggplot(ispcrime_tbl2, aes(x = year, y = violentCrime, color = region)) +
  stat_summary(geom="line", fun.y="sum") +
  labs(title = "Violent crime trend by region", x = "Year", y = "Count") +
  theme_classic(base_size = 15)

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# bar plot of violent crime mean count by region
ggplot(ispcrime_tbl2, aes(x = region, y = violentCrime, fill = region)) +
  stat_summary(geom="bar", fun.y="mean") +
  labs(title = "Violent crime count by region", x = "Region", y = "Count") +
  theme_classic(base_size = 15)

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# histogram of violent crime count by county (excluding Cook)
ggplot(filter(ispcrime_tbl2, county != "Cook"), aes(x = violentCrime)) +
  geom_histogram(binwidth = 100) +
  facet_wrap(~ year) +
  labs(title = "Histogram of violent crime count by county per year",
       x = "Violent crime count", y = "Count") +
  theme_classic(base_size = 15)

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Statistical modeling

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Source: pixabay

And more!

Reports

Slideshow

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Dashboard

Website

Objectives

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Technical objectives

Import and manipulate tabular data files using R;
Create simple data visualizations to extract insight from data using R;
Perform basic statistical analysis using R;
Generate a report on a simple data analysis task using R

Fundamental objectives

Understand the basic elements of the R programming language;
Employ the programmatic approach to research and data analysis projects; and
Leverage online resources to find solutions to specific questions on using R for a given task.

Structure

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Overall setup

Six modules
One module per week
Each module consists of two parts
- except the first module on introduction
All workshop materials (slides and notes) will be available
I will be available, too, for answering questions

Modules

Introduction to R
R basics
Data analysis in R
Data visualization in R
Statistical modeling in R
Sharing your analysis and more

Questions?

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Source: Giphy.com